# Copyright 2022 Lunar Ring. All rights reserved. # Written by Johannes Stelzer, email stelzer@lunar-ring.ai twitter @j_stelzer # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import numpy as np import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union from utils import interpolate_spherical from diffusers import DiffusionPipeline, StableDiffusionControlNetPipeline, ControlNetModel from diffusers.models.attention_processor import ( AttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor, ) from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import retrieve_timesteps warnings.filterwarnings('ignore') torch.backends.cudnn.benchmark = False torch.set_grad_enabled(False) class DiffusersHolder(): def __init__(self, pipe): # Base settings self.negative_prompt = "" self.guidance_scale = 5.0 self.num_inference_steps = 30 # Check if valid pipe self.pipe = pipe self.device = str(pipe._execution_device) self.init_types() self.width_latent = self.pipe.unet.config.sample_size self.height_latent = self.pipe.unet.config.sample_size self.width_img = self.width_latent * self.pipe.vae_scale_factor self.height_img = self.height_latent * self.pipe.vae_scale_factor self.is_sdxl_turbo = False def init_types(self): assert hasattr(self.pipe, "__class__"), "No valid diffusers pipeline found." assert hasattr(self.pipe.__class__, "__name__"), "No valid diffusers pipeline found." if self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline': self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device) self.use_sd_xl = True prompt_embeds, _, _, _ = self.pipe.encode_prompt("test") else: self.use_sd_xl = False prompt_embeds = self.pipe._encode_prompt("test", self.device, 1, True) self.dtype = prompt_embeds.dtype def set_num_inference_steps(self, num_inference_steps): self.num_inference_steps = num_inference_steps if self.use_sd_xl: self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device) def set_dimensions(self, size_output): s = self.pipe.vae_scale_factor if size_output is None: width = self.pipe.unet.config.sample_size height = self.pipe.unet.config.sample_size else: width, height = size_output self.width_img = int(round(width / s) * s) self.width_latent = int(self.width_img / s) self.height_img = int(round(height / s) * s) self.height_latent = int(self.height_img / s) print(f"set_dimensions to width={width} and height={height}") def set_negative_prompt(self, negative_prompt): r"""Set the negative prompt. Currenty only one negative prompt is supported """ if isinstance(negative_prompt, str): self.negative_prompt = [negative_prompt] else: self.negative_prompt = negative_prompt if len(self.negative_prompt) > 1: self.negative_prompt = [self.negative_prompt[0]] def get_text_embedding(self, prompt): do_classifier_free_guidance = self.guidance_scale > 1 and self.pipe.unet.config.time_cond_proj_dim is None text_embeddings = self.pipe.encode_prompt( prompt=prompt, prompt_2=prompt, device=self.pipe._execution_device, num_images_per_prompt=1, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=self.negative_prompt, negative_prompt_2=self.negative_prompt, prompt_embeds=None, negative_prompt_embeds=None, pooled_prompt_embeds=None, negative_pooled_prompt_embeds=None, lora_scale=None, clip_skip=self.pipe._clip_skip, ) return text_embeddings def get_noise(self, seed=420): latents = self.pipe.prepare_latents( 1, self.pipe.unet.config.in_channels, self.height_img, self.width_img, torch.float16, self.pipe._execution_device, torch.Generator(device=self.device).manual_seed(int(seed)), None, ) return latents @torch.no_grad() def latent2image( self, latents: torch.FloatTensor, output_type="pil"): r""" Returns an image provided a latent representation from diffusion. Args: latents: torch.FloatTensor Result of the diffusion process. output_type: "pil" or "np" """ assert output_type in ["pil", "np"] # make sure the VAE is in float32 mode, as it overflows in float16 needs_upcasting = self.pipe.vae.dtype == torch.float16 and self.pipe.vae.config.force_upcast if needs_upcasting: self.pipe.upcast_vae() latents = latents.to(next(iter(self.pipe.vae.post_quant_conv.parameters())).dtype) image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0] # cast back to fp16 if needed if needs_upcasting: self.pipe.vae.to(dtype=torch.float16) image = self.pipe.image_processor.postprocess(image, output_type=output_type)[0] return image def prepare_mixing(self, mixing_coeffs, list_latents_mixing): if type(mixing_coeffs) == float: list_mixing_coeffs = (1 + self.num_inference_steps) * [mixing_coeffs] elif type(mixing_coeffs) == list: assert len(mixing_coeffs) == self.num_inference_steps, f"len(mixing_coeffs) {len(mixing_coeffs)} != self.num_inference_steps {self.num_inference_steps}" list_mixing_coeffs = mixing_coeffs else: raise ValueError("mixing_coeffs should be float or list with len=num_inference_steps") if np.sum(list_mixing_coeffs) > 0: assert len(list_latents_mixing) == self.num_inference_steps, f"len(list_latents_mixing) {len(list_latents_mixing)} != self.num_inference_steps {self.num_inference_steps}" return list_mixing_coeffs @torch.no_grad() def run_diffusion( self, text_embeddings: torch.FloatTensor, latents_start: torch.FloatTensor, idx_start: int = 0, list_latents_mixing=None, mixing_coeffs=0.0, return_image: Optional[bool] = False): if self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline': return self.run_diffusion_sd_xl(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image) elif self.pipe.__class__.__name__ == 'StableDiffusionPipeline': return self.run_diffusion_sd12x(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image) elif self.pipe.__class__.__name__ == 'StableDiffusionControlNetPipeline': pass @torch.no_grad() def run_diffusion_sd12x( self, text_embeddings: torch.FloatTensor, latents_start: torch.FloatTensor, idx_start: int = 0, list_latents_mixing=None, mixing_coeffs=0.0, return_image: Optional[bool] = False): list_mixing_coeffs = self.prepare_mixing() do_classifier_free_guidance = self.guidance_scale > 1.0 # accomodate different sd model types self.pipe.scheduler.set_timesteps(self.num_inference_steps - 1, device=self.device) timesteps = self.pipe.scheduler.timesteps if len(timesteps) != self.num_inference_steps: self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device) timesteps = self.pipe.scheduler.timesteps latents = latents_start.clone() list_latents_out = [] for i, t in enumerate(timesteps): # Set the right starting latents if i == idx_start: latents = latents_start.clone() # Mix latents if i > 0 and list_mixing_coeffs[i] > 0: latents_mixtarget = list_latents_mixing[i - 1].clone() latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i]) if i < idx_start: list_latents_out.append(latents) # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.pipe.unet( latent_model_input, t, encoder_hidden_states=text_embeddings, return_dict=False, )[0] if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.pipe.scheduler.step(noise_pred, t, latents, return_dict=False)[0] list_latents_out.append(latents.clone()) if return_image: return self.latent2image(latents) else: return list_latents_out @torch.no_grad() def run_diffusion_controlnet( self, conditioning: list, latents_start: torch.FloatTensor, idx_start: int = 0, list_latents_mixing=None, mixing_coeffs=0.0, return_image: Optional[bool] = False): prompt_embeds = conditioning[0] image = conditioning[1] list_mixing_coeffs = self.prepare_mixing() controlnet = self.pipe.controlnet control_guidance_start = [0.0] control_guidance_end = [1.0] guess_mode = False num_images_per_prompt = 1 batch_size = 1 eta = 0.0 controlnet_conditioning_scale = 1.0 # align format for control guidance if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list): control_guidance_start = len(control_guidance_end) * [control_guidance_start] elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list): control_guidance_end = len(control_guidance_start) * [control_guidance_end] # 2. Define call parameters device = self.pipe._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = self.guidance_scale > 1.0 # 4. Prepare image image = self.pipe.prepare_image( image=image, width=None, height=None, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=self.device, dtype=controlnet.dtype, do_classifier_free_guidance=do_classifier_free_guidance, guess_mode=guess_mode, ) height, width = image.shape[-2:] # 5. Prepare timesteps self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device) timesteps = self.pipe.scheduler.timesteps # 6. Prepare latent variables generator = torch.Generator(device=self.device).manual_seed(int(420)) latents = latents_start.clone() list_latents_out = [] # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta) # 7.1 Create tensor stating which controlnets to keep controlnet_keep = [] for i in range(len(timesteps)): keeps = [ 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e) for s, e in zip(control_guidance_start, control_guidance_end) ] controlnet_keep.append(keeps[0] if len(keeps) == 1 else keeps) # 8. Denoising loop for i, t in enumerate(timesteps): if i < idx_start: list_latents_out.append(None) continue elif i == idx_start: latents = latents_start.clone() # Mix latents for crossfeeding if i > 0 and list_mixing_coeffs[i] > 0: latents_mixtarget = list_latents_mixing[i - 1].clone() latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i]) # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t) control_model_input = latent_model_input controlnet_prompt_embeds = prompt_embeds if isinstance(controlnet_keep[i], list): cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])] else: cond_scale = controlnet_conditioning_scale * controlnet_keep[i] down_block_res_samples, mid_block_res_sample = self.pipe.controlnet( control_model_input, t, encoder_hidden_states=controlnet_prompt_embeds, controlnet_cond=image, conditioning_scale=cond_scale, guess_mode=guess_mode, return_dict=False, ) if guess_mode and do_classifier_free_guidance: # Infered ControlNet only for the conditional batch. # To apply the output of ControlNet to both the unconditional and conditional batches, # add 0 to the unconditional batch to keep it unchanged. down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples] mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample]) # predict the noise residual noise_pred = self.pipe.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=None, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # Append latents list_latents_out.append(latents.clone()) if return_image: return self.latent2image(latents) else: return list_latents_out @torch.no_grad() def run_diffusion_sd_xl_turbo( self, text_embeddings: list, latents_start: torch.FloatTensor, idx_start: int = 0, list_latents_mixing=None, mixing_coeffs=0.0, return_image: Optional[bool] = False, seed=420, **kwargs, ): timesteps = None denoising_end = None guidance_scale = 0.0 negative_prompt = None negative_prompt_2 = None num_images_per_prompt = 1 eta = 0.0 latents = None prompt_embeds = None negative_prompt_embeds = None pooled_prompt_embeds = None negative_pooled_prompt_embeds = None ip_adapter_image = None return_dict = True cross_attention_kwargs = None guidance_rescale = 0.0 original_size = None crops_coords_top_left = (0, 0) target_size = None negative_original_size = None negative_crops_coords_top_left = (0, 0) negative_target_size = None clip_skip = None # 0. Default height and width to unet height = self.pipe.default_sample_size * self.pipe.vae_scale_factor width = self.pipe.default_sample_size * self.pipe.vae_scale_factor list_mixing_coeffs = self.prepare_mixing(mixing_coeffs, list_latents_mixing) original_size = (height, width) target_size = (height, width) # 1. (skipped) Check inputs. Raise error if not correct self.pipe._guidance_scale = guidance_scale self.pipe._guidance_rescale = guidance_rescale self.pipe._clip_skip = clip_skip self.pipe._cross_attention_kwargs = cross_attention_kwargs self.pipe._denoising_end = denoising_end self.pipe._interrupt = False # 2. Define call parameters batch_size = 1 device = self.pipe._execution_device # 3. Encode input prompt prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = text_embeddings # 4. Prepare timesteps timesteps, self.num_inference_steps = retrieve_timesteps(self.pipe.scheduler, self.num_inference_steps, device, timesteps) # 5. Prepare latent variables latents = latents_start.clone() list_latents_out = [] # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(torch.Generator(device=self.device).manual_seed(int(0)), eta) # 7. Prepare added time ids & embeddings add_text_embeds = pooled_prompt_embeds if self.pipe.text_encoder_2 is None: text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) else: text_encoder_projection_dim = self.pipe.text_encoder_2.config.projection_dim add_time_ids = self.pipe._get_add_time_ids( original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim, ) if negative_original_size is not None and negative_target_size is not None: negative_add_time_ids = self.pipe._get_add_time_ids( negative_original_size, negative_crops_coords_top_left, negative_target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim, ) else: negative_add_time_ids = add_time_ids if self.pipe.do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0) prompt_embeds = prompt_embeds.to(device) add_text_embeds = add_text_embeds.to(device) add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) if ip_adapter_image is not None: output_hidden_state = False if isinstance(self.pipe.unet.encoder_hid_proj, ImageProjection) else True image_embeds, negative_image_embeds = self.pipe.encode_image( ip_adapter_image, device, num_images_per_prompt, output_hidden_state ) if self.pipe.do_classifier_free_guidance: image_embeds = torch.cat([negative_image_embeds, image_embeds]) image_embeds = image_embeds.to(device) # 8. Denoising loop num_warmup_steps = max(len(timesteps) - self.num_inference_steps * self.pipe.scheduler.order, 0) # 9. Optionally get Guidance Scale Embedding timestep_cond = None if self.pipe.unet.config.time_cond_proj_dim is not None: guidance_scale_tensor = torch.tensor(self.pipe.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) timestep_cond = self.pipe.get_guidance_scale_embedding( guidance_scale_tensor, embedding_dim=self.pipe.unet.config.time_cond_proj_dim ).to(device=device, dtype=latents.dtype) self.pipe._num_timesteps = len(timesteps) for i, t in enumerate(timesteps): # Set the right starting latents # Write latents out and skip if i < idx_start: list_latents_out.append(None) continue elif i == idx_start: latents = latents_start.clone() # Mix latents for crossfeeding if i > 0 and list_mixing_coeffs[i] > 0: latents_mixtarget = list_latents_mixing[i - 1].clone() latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i]) # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if self.pipe.do_classifier_free_guidance else latents latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} if ip_adapter_image is not None: added_cond_kwargs["image_embeds"] = image_embeds noise_pred = self.pipe.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, timestep_cond=timestep_cond, cross_attention_kwargs=self.pipe.cross_attention_kwargs, added_cond_kwargs=added_cond_kwargs, return_dict=False, )[0] # perform guidance if self.pipe.do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + self.pipe.guidance_scale * (noise_pred_text - noise_pred_uncond) if self.pipe.do_classifier_free_guidance and self.pipe.guidance_rescale > 0.0: # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.pipe.guidance_rescale) # compute the previous noisy sample x_t -> x_t-1 latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # Append latents list_latents_out.append(latents.clone()) if return_image: return self.latent2image(latents) else: return list_latents_out @torch.no_grad() def run_diffusion_sd_xl( self, text_embeddings: tuple, latents_start: torch.FloatTensor, idx_start: int = 0, list_latents_mixing=None, mixing_coeffs=0.0, return_image: Optional[bool] = False, ): prompt_2 = None height = None width = None timesteps = None denoising_end = None negative_prompt_2 = None num_images_per_prompt = 1 eta = 0.0 generator = None latents = None prompt_embeds = None negative_prompt_embeds = None pooled_prompt_embeds = None negative_pooled_prompt_embeds = None ip_adapter_image = None output_type = "pil" return_dict = True cross_attention_kwargs = None guidance_rescale = 0.0 original_size = None crops_coords_top_left = (0, 0) target_size = None negative_original_size = None negative_crops_coords_top_left = (0, 0) negative_target_size = None clip_skip = None callback = None callback_on_step_end = None callback_on_step_end_tensor_inputs = ["latents"] # kwargs are additional keyword arguments and don't need a default value set here. # 0. Default height and width to unet height = height or self.pipe.default_sample_size * self.pipe.vae_scale_factor width = width or self.pipe.default_sample_size * self.pipe.vae_scale_factor original_size = original_size or (height, width) target_size = target_size or (height, width) # 1. Check inputs. skipped. self.pipe._guidance_scale = self.guidance_scale self.pipe._guidance_rescale = guidance_rescale self.pipe._clip_skip = clip_skip self.pipe._cross_attention_kwargs = cross_attention_kwargs self.pipe._denoising_end = denoising_end self.pipe._interrupt = False # 2. Define call parameters list_mixing_coeffs = self.prepare_mixing(mixing_coeffs, list_latents_mixing) batch_size = 1 device = self.pipe._execution_device # 3. Encode input prompt lora_scale = None ( prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, ) = text_embeddings # 4. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps(self.pipe.scheduler, self.num_inference_steps, device, timesteps) # 5. Prepare latent variables num_channels_latents = self.pipe.unet.config.in_channels latents = latents_start.clone() list_latents_out = [] # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta) # 7. Prepare added time ids & embeddings add_text_embeds = pooled_prompt_embeds if self.pipe.text_encoder_2 is None: text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) else: text_encoder_projection_dim = self.pipe.text_encoder_2.config.projection_dim add_time_ids = self.pipe._get_add_time_ids( original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim, ) if negative_original_size is not None and negative_target_size is not None: negative_add_time_ids = self.pipe._get_add_time_ids( negative_original_size, negative_crops_coords_top_left, negative_target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim, ) else: negative_add_time_ids = add_time_ids if self.pipe.do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0) prompt_embeds = prompt_embeds.to(device) add_text_embeds = add_text_embeds.to(device) add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) if ip_adapter_image is not None: output_hidden_state = False if isinstance(self.pipe.unet.encoder_hid_proj, ImageProjection) else True image_embeds, negative_image_embeds = self.pipe.encode_image( ip_adapter_image, device, num_images_per_prompt, output_hidden_state ) if self.pipe.do_classifier_free_guidance: image_embeds = torch.cat([negative_image_embeds, image_embeds]) image_embeds = image_embeds.to(device) # 8. Denoising loop num_warmup_steps = max(len(timesteps) - num_inference_steps * self.pipe.scheduler.order, 0) # 9. Optionally get Guidance Scale Embedding timestep_cond = None if self.pipe.unet.config.time_cond_proj_dim is not None: guidance_scale_tensor = torch.tensor(self.pipe.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) timestep_cond = self.pipe.get_guidance_scale_embedding( guidance_scale_tensor, embedding_dim=self.pipe.unet.config.time_cond_proj_dim ).to(device=device, dtype=latents.dtype) self.pipe._num_timesteps = len(timesteps) for i, t in enumerate(timesteps): # Set the right starting latents # Write latents out and skip if i < idx_start: list_latents_out.append(None) continue elif i == idx_start: latents = latents_start.clone() # Mix latents for crossfeeding if i > 0 and list_mixing_coeffs[i] > 0: latents_mixtarget = list_latents_mixing[i - 1].clone() latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i]) # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if self.pipe.do_classifier_free_guidance else latents latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} if ip_adapter_image is not None: added_cond_kwargs["image_embeds"] = image_embeds noise_pred = self.pipe.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, timestep_cond=timestep_cond, cross_attention_kwargs=self.pipe.cross_attention_kwargs, added_cond_kwargs=added_cond_kwargs, return_dict=False, )[0] # perform guidance if self.pipe.do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + self.pipe.guidance_scale * (noise_pred_text - noise_pred_uncond) if self.pipe.do_classifier_free_guidance and self.pipe.guidance_rescale > 0.0: # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.pipe.guidance_rescale) # compute the previous noisy sample x_t -> x_t-1 latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # Append latents list_latents_out.append(latents.clone()) if return_image: return self.latent2image(latents) else: return list_latents_out #%% if __name__ == "__main__": from PIL import Image from diffusers import AutoencoderTiny pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0" # pretrained_model_name_or_path = "stabilityai/sdxl-turbo" pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16") pipe.to("cuda") #% # pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16) # pipe.vae = pipe.vae.cuda() #%% resanity import time self = DiffusersHolder(pipe) prompt1 = "photo of underwater landscape, fish, und the sea, incredible detail, high resolution" negative_prompt = "blurry, ugly, pale" num_inference_steps = 30 guidance_scale = 4 self.set_num_inference_steps(num_inference_steps) self.guidance_scale = guidance_scale prefix='full' for i in range(10): self.set_negative_prompt(negative_prompt) text_embeddings = self.get_text_embedding(prompt1) latents_start = self.get_noise(np.random.randint(111111)) t0 = time.time() # img_refx = self.pipe(prompt=prompt1, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale)[0] img_refx = self.run_diffusion_sd_xl_resanity(text_embeddings=text_embeddings, latents_start=latents_start, return_image=True) dt_ref = time.time() - t0 img_refx.save(f"x_{prefix}_{i}.jpg") # xxx # self.set_negative_prompt(negative_prompt) # self.set_num_inference_steps(num_inference_steps) # text_embeddings1 = self.get_text_embedding(prompt1) # prompt_embeds1, negative_prompt_embeds1, pooled_prompt_embeds1, negative_pooled_prompt_embeds1 = text_embeddings1 # latents_start = self.get_noise(420) # t0 = time.time() # img_dh = self.run_diffusion_sd_xl_resanity(text_embeddings1, latents_start, idx_start=0, return_image=True) # dt_dh = time.time() - t0 """ sth bad in call sth bad in cond sth bad in noise """ # xxxx # #%% # self = DiffusersHolder(pipe) # num_inference_steps = 4 # self.set_num_inference_steps(num_inference_steps) # latents_start = self.get_noise(420) # guidance_scale = 0 # self.guidance_scale = 0 # #% get embeddings1 # prompt1 = "Photo of a colorful landscape with a blue sky with clouds" # text_embeddings1 = self.get_text_embedding(prompt1) # prompt_embeds1, negative_prompt_embeds1, pooled_prompt_embeds1, negative_pooled_prompt_embeds1 = text_embeddings1 # #% get embeddings2 # prompt2 = "Photo of a tree" # text_embeddings2 = self.get_text_embedding(prompt2) # prompt_embeds2, negative_prompt_embeds2, pooled_prompt_embeds2, negative_pooled_prompt_embeds2 = text_embeddings2 # latents1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=False) # img1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True) # img1B = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True) # # latents2 = self.run_diffusion_sd_xl(text_embeddings2, latents_start, idx_start=0, return_image=False) # # # check if brings same image if restarted # # img1_return = self.run_diffusion_sd_xl(text_embeddings1, latents1[idx_mix-1], idx_start=idx_start, return_image=True) # # mix latents # #%% # idx_mix = 2 # fract=0.8 # latents_start_mixed = interpolate_spherical(latents1[idx_mix-1], latents2[idx_mix-1], fract) # prompt_embeds = interpolate_spherical(prompt_embeds1, prompt_embeds2, fract) # pooled_prompt_embeds = interpolate_spherical(pooled_prompt_embeds1, pooled_prompt_embeds2, fract) # negative_prompt_embeds = negative_prompt_embeds1 # negative_pooled_prompt_embeds = negative_pooled_prompt_embeds1 # text_embeddings_mix = [prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds] # self.run_diffusion_sd_xl(text_embeddings_mix, latents_start_mixed, idx_start=idx_start, return_image=True)